SOTAVerified

3D Object Detection

3D Object Detection is a task in computer vision where the goal is to identify and locate objects in a 3D environment based on their shape, location, and orientation. It involves detecting the presence of objects and determining their location in the 3D space in real-time. This task is crucial for applications such as autonomous vehicles, robotics, and augmented reality.

( Image credit: AVOD )

Papers

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Showing 276–300 of 1576 papers

TitleStatusHype
Far3D: Expanding the Horizon for Surround-view 3D Object DetectionCode1
ImGeoNet: Image-induced Geometry-aware Voxel Representation for Multi-view 3D Object DetectionCode1
GPA-3D: Geometry-aware Prototype Alignment for Unsupervised Domain Adaptive 3D Object Detection from Point CloudsCode1
MS3D++: Ensemble of Experts for Multi-Source Unsupervised Domain Adaption in 3D Object DetectionCode1
V-DETR: DETR with Vertex Relative Position Encoding for 3D Object DetectionCode1
PARTNER: Level up the Polar Representation for LiDAR 3D Object DetectionCode1
Spatio-Temporal Domain Awareness for Multi-Agent Collaborative PerceptionCode1
PG-RCNN: Semantic Surface Point Generation for 3D Object DetectionCode1
HVDetFusion: A Simple and Robust Camera-Radar Fusion FrameworkCode1
CORE: Cooperative Reconstruction for Multi-Agent PerceptionCode1
SA-BEV: Generating Semantic-Aware Bird's-Eye-View Feature for Multi-view 3D Object DetectionCode1
RCM-Fusion: Radar-Camera Multi-Level Fusion for 3D Object DetectionCode1
Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-LabelingCode1
Practical Collaborative Perception: A Framework for Asynchronous and Multi-Agent 3D Object DetectionCode1
Spatial-Temporal Graph Enhanced DETR Towards Multi-Frame 3D Object DetectionCode1
Tame a Wild Camera: In-the-Wild Monocular Camera CalibrationCode1
Predict to Detect: Prediction-guided 3D Object Detection using Sequential ImagesCode1
MoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud SequencesCode1
Point-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color ContrastCode1
Radar Enlighten the Dark: Enhancing Low-Visibility Perception for Automated Vehicles with Camera-Radar FusionCode1
Learning Occupancy for Monocular 3D Object DetectionCode1
Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation ModelsCode1
SSD-MonoDETR: Supervised Scale-aware Deformable Transformer for Monocular 3D Object DetectionCode1
Multi-Modal 3D Object Detection by Box MatchingCode1
3D Small Object Detection with Dynamic Spatial PruningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1EA-LSSNDS0.78—Unverified
2MMFusion-eNDS0.77—Unverified
3MegFusionNDS0.77—Unverified
4RacoonPowerNDS0.76—Unverified
5BEVFusion-eNDS0.76—Unverified
6DeepInteraction-largeNDS0.76—Unverified
7DeepInteraction-eNDS0.76—Unverified
8DAANDS0.75—Unverified
9FusionVPENDS0.75—Unverified
10CenterPoint-FusionNDS0.75—Unverified